詳細情報
| 著作分類 | 論文 |
|---|---|
| 著者 | Delios, Andrew：Elena Giulia Clemente： Tao Wu：Hongbin Tan：Yong Wang：Michael Gordon：Domenico Viganola：Zhaowei Chen：Anna Dreber：Magnus Johannesson：Thomas Pfeiffer：Generalizability Tests Forecasting Collaboration：Eric Luis Uhlmann |
| 論文タイトル | Examining the generalizability of research findings from archival data |
| 機関名 | Proceedings of the National Academy of Sciences |
| 巻 | 119巻 |
| 号 | 30号 |
| ページ | |
| 出版・発行年月 | 2022/07/19 |
| 要旨 | This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples. |
| 備考 | |
| 参考URL | https://www.pnas.org/doi/10.1073/pnas.2120377119 |
| ラベル | 戦略論, 方法論 |
| 登録日 | 2022/07/19 |
